R Notebook
Group - Shreya Shirodkar, Abhijeet Deshmukh
You will work on a dataset on the geographic distribution of COVID-19 cases worldwide that was obtained from the European Centre for Disease Prevention and Control (ECDC). The data provides the daily number of new cases for each country (NOTE: it is not the cumulative number of cases).
- (0 pts) Download the COVID-19 case distribution (in XML formal): https://opendata.ecdc.europa.eu/covid19/casedistribution/xml/. Load the XML file into a browser or text editing tool and inspect it. Explore the data set as you see fit and get a sense of the data. Note that you are working with a live dataset that is updated daily. Therefore it is best to ensure that your results are data driven.
library(XML)
#url <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/xml/"
#download.file(url=url,"C:/Users/SHREYA/Desktop/DA5020/Youth_Unemployment/url.xml" )
#path <- "C:/Users/SHREYA/Desktop/DA5020/Youth_Unemployment/"
#setwd(path)
#xmlToDataFrame("url.xml")- (15 pts) Load the data into R (directly from the URL) and create two linked tibbles: one for country and the other for covid_19_data that contains each country’s reported case. The country tibble should contain the following: countriesAndTerritories, countryterritoryCode (primary key), popData2018, continentExp. The covid_19_data tibble should contain: id (auto incremented value that will serve as the primary key), countryterritoryCode(foreign key), dateRep, cases, deaths. You can link/join both tibbles by using the countryterritoryCode.
# Loading the libraries in R
library(httr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse)## -- Attaching packages ------------------------------------------------------------ tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.4
## v tibble 3.0.1 v stringr 1.4.0
## v tidyr 1.1.0 v forcats 0.5.0
## v readr 1.3.1## -- Conflicts --------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(countrycode)
# Storing the URL of the source in url
url <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/xml/"
# Parsing the data in URL
doc <- htmlParse(rawToChar(GET(url)$content))
# Obtaining the root of the parsed data
root <- xmlRoot(doc)
# Loading the xml data in data frame using the root
data<- xmlToDataFrame(nodes = xmlChildren(xmlRoot(doc)[["body"]][["records"]]))
head(data)nrow(data)## [1] 23428## Tidying the original data set
# filtering the rows with missing country codes
missing_code <- data %>% filter(countryterritorycode == "")
missing_code# storing the country names of the rows with missing country codes in y
y <- missing_code$countriesandterritories
# Using countrycode library to obtain the country codes of missing code
codes<- countryname(y, destination = 'iso3c')
# adding the country codes back to the missing_code data frame
missing_code$countryterritorycode <- codes
missing_code# binding the rows of missing_code to the original data set
data <- rbind(missing_code,data)
head(data)# Storing the blank observations as NA in the original data frame
data[data==""] <- NA
# Dropping the rows with NA in country territory variable
data <- data %>% drop_na(countryterritorycode)
nrow(data)## [1] 23428https://www.worldometers.info/world-population/anguilla-population/ https://www.worldometers.info/world-population/western-sahara-population/ https://www.worldometers.info/world-population/falkland-islands-malvinas-population/ https://www.worldometers.info/world-population/caribbean-netherlands-population/ https://www.worldometers.info/world-population/eritrea-population/ https://en.wikipedia.org/wiki/Caribbean_Netherlands
library(dplyr)
library(tidyverse)
#install.packages("countrycode")
# Making the tibble country with the variables specified in the question
country<- data%>% select(countriesandterritories,countryterritorycode, popdata2018,continentexp ) %>% distinct
country <- as_tibble(country )
head(country)# Checking the count of primary key countryterritorycode
country %>%
count(countryterritorycode) %>%
filter(n > 1)# Checking the NA in country data set
colSums(is.na(country))## countriesandterritories countryterritorycode popdata2018
## 0 0 5
## continentexp
## 0# Filtering the rows with missing popdata2018
pop_missing<- country %>% filter(is.na(popdata2018))
pop_missing# Vector of populations of the country with missing popdata2018
pop <- c(14731,25711,3234,567407, 3452786)
# Adding the popdata2018 to the pop_missing data frame
pop_missing$popdata2018 <- pop
pop_missing# binding the pop_missing data to country data frame
country <- rbind(pop_missing,country)
head(country)# dropping NA from the country data frame
country<- country %>% drop_na(popdata2018)
head(country)library(dplyr)
# Creating a tibble with data specified in the question
covid_19_data <- data %>% select(countryterritorycode,daterep,cases, deaths)
covid_19_data<- as_tibble(covid_19_data)
# Adding ID column to the data set
covid_19_data<- tibble::rowid_to_column(covid_19_data, "ID")
covid_19_data# Checking the count of foreign keys
covid_19_data %>%
count(ID) %>%
filter(n > 1)# Joining the data to obtain a tidy data of covid cases
data_covid <- left_join(country,covid_19_data, by ="countryterritorycode")
# Rearranging the variables
data_covid <- data_covid %>% select(ID,countriesandterritories,countryterritorycode,continentexp,daterep,cases,deaths,popdata2018)
head(data_covid)#Checkng the missing values
colSums(is.na(data_covid))## ID countriesandterritories countryterritorycode
## 0 0 0
## continentexp daterep cases
## 0 0 0
## deaths popdata2018
## 0 0# Changing the data types of the variables
data_covid$cases <- as.numeric(as.character(data_covid$cases))
data_covid$deaths <- as.numeric(as.character(data_covid$deaths))
data_covid$popdata2018 <- as.numeric(as.character(data_covid$popdata2018))
# Checking the outliers in cases i.e the negative case values
cases_negative<- data_covid %>% filter(cases<0)
cases_negative# Deleting the rows with negative case values
data_covid <- data_covid[-cases_negative$ID, ]
# Converting the negative cases to positive
cases_negative$cases <- -(cases_negative$cases)
# Joining the positive converted cases back to the data_covid data frame (I'm assuming that the negative sign is an error)
data_covid <- rbind(cases_negative, data_covid)
data_covid# Similar steps for death cases
death_negative <- data_covid %>% filter(deaths <0)
death_negativedata_covid <- data_covid[-death_negative$ID, ]
death_negative$cases <- -(death_negative$cases)
data_covid <- rbind(death_negative,data_covid)
data_covid(10 pts) Create a function called worldwideCases() that displays: a) the total cases worldwide, b) the number of new cases within the past day (grouped by continent).
#install.packages("lubridate")
data1 <- data_covid
#loading data in R
library(dplyr)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:dplyr':
##
## intersect, setdiff, union## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union# Creating function to display total cases worldwide and number of new cases within the past day
worldwideCases <- function(x)
{
# Total cases of covid
total_cases <- sum(x$cases)
# Obtaining the present date
step1 <- today()
# Obtaining the past day date
step2 <- step1 -1
# Using mutate to format the date year-month-day format to match the date in step 2
x <- x %>% mutate(date_formatted =format(as.POSIXct(x$daterep,format='%d/%m/%Y'),format='%Y-%m-%d'))
# filtering the date that match the step2 and grouping by continent to obtain the new cases within the past day
new_cases <- x %>% filter(date_formatted == step2) %>% group_by(continentexp) %>% summarise(new_cases = sum(cases))
# coverting to list to add in return
results <- list("worldwide"= total_cases, "new cases" = new_cases)
return(results)
}
# Giving the data frame as the argument to the function
worldwideCases(data1)## $worldwide
## [1] 7767721
##
## $`new cases`
## # A tibble: 5 x 2
## continentexp new_cases
## <chr> <dbl>
## 1 Africa 8581
## 2 America 79303
## 3 Asia 38380
## 4 Europe 18154
## 5 Oceania 8- (10 pts) Create visualizations that show the progression of the cases and the mortality rate in each continent. https://www-doh.state.nj.us/doh-shad/view/sharedstatic/CauseSpecificDeathRate.pdf https://www.globalhealthlearning.org/taxonomy/term/1583
# creating a variable called data_formatted in which the format of the date is month/date/year to arrange the date. adding variable mortality_rate
data2<- data_covid %>% mutate(month =(format(as.POSIXct(data_covid$daterep,format='%d/%m/%Y'),format='%m'))) %>% mutate(date_formatted = (format(as.POSIXct(data_covid$daterep,format='%d/%m/%Y'),format='%m/%d/%y'))) %>% arrange(date_formatted) %>% group_by(month,continentexp) %>% summarise(death = sum(deaths), case = sum(cases), population = first(popdata2018)) %>% mutate(mortality_rate = (death/population)*100) %>% filter(month != 12)
library(ggplot2)
# Plot of progression of cases and mortality rate in each continent
a <- ggplot(data2) %>% + geom_col(aes(x=month, y= case, group = 1)) +facet_wrap(~data2$continentexp) + theme(axis.text.x = element_text(angle = 90, size = 8, color = "red")) + labs(title = "Progression of cases", caption = "This Graph shows the progression of cases in each continent")
b <- ggplot(data2) + geom_line(aes(x=month, y= mortality_rate, color = continentexp, group = 1)) + facet_wrap(~data2$continentexp)+ ylim(0,4) + labs(title = "Progression of mortality rate", caption = "This Graph shows the progression of mortality rate in each continent")
#a + geom_line(aes(x=month, y= mortality_rate, color = continentexp, group = 1)) +scale_y_continuous(sec.axis = sec_axis(~./100000, name = "mortality rate"))
#require(gridExtra)
#install.packages("gridExtra")
#grid.arrange(a,b)
a
b
Progression of cases: The graph traces the months of January to June which shows the progression of Covid-19 cases, categorized by continents. It can be inferred that the continents of America and Europe have shown steep rise in cases from the month of march and have attained the peaks in the month of MAy and April respectively, showing downward trend thereafter. Whereas the continents of Africa and Asia have a gradual rise in cases without any considerable peak count in cases.
Progression of Mortality Rate: The graph traces the months of January to June which shows the progression of mortality rates of continents.The graphs are pretty steady, indicating minimal progression in mortality rates, except for the continent of Europe and Africa. Europe shows a very exponential rise in mortality rate from February to March, where it reaches its peak value and decreases thereafter. The African continent shows a pretty small hike in the mortality rate in the month of may.
- (10 pts) Display the ten countries with the highest number of cases. Analyze the data and indicate the date of the first reported case and the ratio of cases-to-fatalities for each country (use supporting visualizations). https://www.britannica.com/science/case-fatality-rate
library(tidyverse)
library(dplyr)
# Grouping the data by countries and summarizing with sum() will give the total of cases of each country followed by arranging the countries in descending order and using slice to obtain the top 10 countries with highest cases of Covid
top_10<- data_covid %>% group_by(countriesandterritories) %>% summarise(total=sum(cases)) %>% arrange(desc(total)) %>% slice(1:10)
top_10# Using left_join() to join the top 10 countries with their respective data from data_covid.
top_10_old<- left_join(top_10,data_covid,by ="countriesandterritories")
top_10new<- top_10_old %>%
# Using mutate to change the format of dates to arrange the dates
mutate(date_formatted =format(as.POSIXct(top_10_old$daterep,format='%d/%m/%Y'),format='%m/%d/%Y')) %>%
# sorting the dates in ascending
arrange(date_formatted) %>%
# filtering out the dates with zero cases
filter(cases != 0) %>%
# grouping by countries and using summarize to obtain the first(daterep of each country), getting the sum of cases and death to find the case to fatality ratio
group_by(countriesandterritories) %>% summarise(first_case = first(date_formatted), total_cases = sum(cases), total_deaths = sum(deaths)) %>% mutate(case_to_fatality = (total_deaths/total_cases)*100)
top_10new# Plot of progression of total_case to case_to_fatality of top 10 countries
library(ggplot2)
top_10new %>% ggplot(mapping = aes(x= total_cases, y= case_to_fatality, color = countriesandterritories)) + geom_point() + labs(title = "Plot of total cases and case_to_fatality", caption = "This Graph show the progression of cases and case_to _fatality ", x = "total cases (people)") 
# Plot of the top 10 countries and total cases
top_10 %>% ggplot(mapping = aes(x=countriesandterritories, y=total, fill= countriesandterritories))+ geom_bar(stat="identity")+ labs(title = "Top 10 countries and the total cases",caption = "This graph displays the top 10 countries with highest cases", x= "country", y = "total cases(people)" ) + theme(axis.text.x = element_text(angle = 90, size = 7, color = "black"))
# Plot of cases vs death
top_10_old %>%ggplot(mapping = aes(x=cases, y= deaths, color = countriesandterritories
)) + geom_point(size = 0.5) + ylim(0,2500) + facet_wrap(~top_10_old$countriesandterritories) + labs(title = "Plot of cases and deaths",caption = "This plot displays the cases and deaths in the top 10 countries with highest cases", x= "cases(people)", y = "death(people)" ) + theme(axis.text.x = element_text(angle = 90, size = 7, color = "black"))## Warning: Removed 7 rows containing missing values (geom_point).
# Plot of first reported case and countries
top_10new %>% arrange(first_case) %>% ggplot(mapping = aes(x=countriesandterritories, y=first_case, fill= countriesandterritories))+ geom_bar(stat="identity")+ labs(title = "Dates of first reported cases and countries",caption = "Displays the dates of first reported cases in the top 10 countries with highest cases of covid-19", x= "Country", y = "First cases(dates in %m/%d/%Y)" ) + theme(axis.text.x = element_text(angle = 90, size = 7, color = "black"))
- (10 pts) Use the mutate verb in dplyr to calculate the cumulative cases and deaths for each country. The new fields should be named cumulative_cases and cumulative_deaths respectively.
# Using mutate to add a variable called data_formatted which formats the date as %m%d%y to arrange in ascending order. Group the data by countries. Using cumsum() to obtain the cumulative cases and death for each country.
df <- data_covid %>% mutate(date_formatted =format(as.POSIXct(data_covid$daterep,format='%d/%m/%Y'),format='%m/%d/%Y')) %>% arrange(date_formatted)%>% group_by(countriesandterritories) %>% mutate(cumulative_cases = cumsum(cases), cumulative_deaths = cumsum(deaths)) %>% arrange(countriesandterritories)
df# Filtering the country India to check the output variables cumulative cases and death of a country (Example)
df %>% filter(countriesandterritories == "India")- (15 pts) Create a function called, casesByCountry(), that takes a user defined date and country code as its arguments and displays the distribution of cases for the selected country, leading up to the chosen date. Annotate the chart to show the highest number of cases that were reported; this should correspond with the daily reported case and not the cumulative cases. The chart should also contain a subtitle that indicates the population for the selected country. Note: if a date is not specified, make the current date the default. If a country is not specified, display a message to the user.
library(ggplot2)
# Creating the function
casesByCountry <- function(x,y)
{
# using missing() to check if the date argument is missing
if(missing(x))
{
# Using the current date in the argumet is missing
date<- Sys.Date()
# Formatting the date
date<- format(y, format = "%m/%d/%Y")
}
else{
# Using the date provided in the argument when date is given
date <- x
# formatting the date
date <- format(as.Date(date,format="%d/%m/%y"),format='%m/%d/%Y')
}
# Checking if the country code argument is missing
if(missing(y))
{
# displaying the message when the argument country code is missing
message <- "Country Code missing: Please enter a country code to obtain the distribution of cases of the country"
return(message)
}
else
{
# filtering the data of the country in argument, arranging the date and filtering all the dates upto the date in the argument
for_plot <- data_covid %>% mutate(date_formatted =format(as.POSIXct(data_covid$daterep,format='%d/%m/%Y'),format='%m/%d/%Y'))%>% filter(countryterritorycode == y) %>% arrange(date_formatted) %>% filter(date_formatted <= date)
# filtering the highest number of case reported in the data
max_cases<- for_plot %>% filter(cases == max(cases))
#Removing all the columns with zero cases till the first non-zero case
# Using mutate to obtain the row with first non-zero case and filter all rows from non-zero cases, removing the column created with mutate
for_plot <-for_plot %>% mutate(first_match = min(row_number()[cases != 0])) %>% filter(row_number() >= first_match) %>% select(-first_match) %>% ungroup()
# barPlot of dates and cases. annotated with the highest cases
a <- ggplot(data = for_plot, mapping = aes(x =date_formatted, y = cases, fill = countryterritorycode )) + geom_bar(stat = "identity") + annotate("text", x= max_cases$date_formatted, y =max_cases$cases, label = paste("max =",max_cases$cases),color="blue", fontface="italic" ) +theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
# Adding label, subtitle = population of the country and adujusting the size and font of the texts
graph<- a + labs(title = paste("Distribution of cases in",for_plot$countriesandterritories), subtitle = paste("population= ",for_plot$popdata2018[1]),caption = "Data source: European Centre for Disease Prevention and Control (ECDC)") + theme(axis.text=element_text(size=12), axis.title=element_text(size=14,face="bold")) + theme(axis.text.x = element_text(angle = 90, size = 5, color = "blue"))
result <- list(for_plot, graph)
return(result)
}
}
# passing arguments viz. date and countrycode
date <- "05/05/2020"
countrycode <- "ABW"
casesByCountry(date,countrycode)## [[1]]
## # A tibble: 44 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 1300 Aruba ABW America 13/03/~ 2 0
## 2 1299 Aruba ABW America 20/03/~ 2 0
## 3 1298 Aruba ABW America 24/03/~ 8 0
## 4 1297 Aruba ABW America 25/03/~ 5 0
## 5 1296 Aruba ABW America 26/03/~ 2 0
## 6 1295 Aruba ABW America 27/03/~ 9 0
## 7 1294 Aruba ABW America 28/03/~ 0 0
## 8 1293 Aruba ABW America 29/03/~ 0 0
## 9 1292 Aruba ABW America 30/03/~ 22 0
## 10 1291 Aruba ABW America 01/04/~ 5 0
## # ... with 34 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
- (30 pts) Select a country, of your choice, and use the casesByCountry() function to show the progression of the total COVID-19 cases to-date. Analyze the chart and the supporting data; indicate the total number of cases that were reported and the date of the first reported case. What is the current trend? • Based on your analysis for this country, what are the potential impact on countries like Andorra and San Marino. Create visualizations to support your analysis and/or add other supporting dataset(s) if necessary.
# Selecting the country Monaco
countrycode <- "MCO"
# Passing the argument country code in the function without date to obtain all cases till date
case_MCO<- casesByCountry( ,countrycode)
case_MCO## [[1]]
## # A tibble: 96 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 14433 Monaco MCO Europe 29/02/~ 1 0
## 2 14432 Monaco MCO Europe 01/03/~ 0 0
## 3 14431 Monaco MCO Europe 02/03/~ 0 0
## 4 14430 Monaco MCO Europe 15/03/~ 2 0
## 5 14429 Monaco MCO Europe 16/03/~ 4 0
## 6 14428 Monaco MCO Europe 17/03/~ 2 0
## 7 14427 Monaco MCO Europe 18/03/~ 0 0
## 8 14426 Monaco MCO Europe 19/03/~ 0 0
## 9 14425 Monaco MCO Europe 20/03/~ 2 0
## 10 14424 Monaco MCO Europe 21/03/~ 1 0
## # ... with 86 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
# Converting the data of the country to data frame
case_MCO1<- as.data.frame(case_MCO[1])
# Obtaining the Total cases that were reported using summarise(sum())
total_cases <- case_MCO1%>% summarise(total_cases = sum(cases))
total_cases# Obtaining the first reported case
first_case<- case_MCO1 %>%
arrange(date_formatted) %>%
filter(cases != 0) %>% summarise(first_reported_case = first(daterep))
first_caseThe cases to date graph shows the maximum value of 9 cases per day and total cases of 99, which is quite low as compared to its neighboring countries of Italy, Spain, France (which we have displayed in the graphs below) and other countries. It can be inferred from the graph that the cases showed its peak during early march and since then it has been declined very rapidly, showing almost no cases or 1 case in the months to follow, displaying a very steep negative trend.
# Checking the distribution of cases in Andorra
countrycode <- "AND"
case_AND<- casesByCountry( ,countrycode)
case_AND## [[1]]
## # A tibble: 93 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 786 Andorra AND Europe 03/03/~ 1 0
## 2 785 Andorra AND Europe 14/03/~ 1 0
## 3 784 Andorra AND Europe 16/03/~ 3 0
## 4 783 Andorra AND Europe 17/03/~ 9 0
## 5 782 Andorra AND Europe 18/03/~ 0 0
## 6 781 Andorra AND Europe 19/03/~ 39 0
## 7 780 Andorra AND Europe 20/03/~ 22 0
## 8 779 Andorra AND Europe 21/03/~ 0 0
## 9 778 Andorra AND Europe 22/03/~ 13 0
## 10 777 Andorra AND Europe 23/03/~ 25 0
## # ... with 83 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
# Checking the distribution of cases in San_marino
countrycode <- "SMR"
casesByCountry( ,countrycode)## [[1]]
## # A tibble: 108 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 18606 San_Marino SMR Europe 28/02/~ 1 0
## 2 18605 San_Marino SMR Europe 29/02/~ 0 0
## 3 18604 San_Marino SMR Europe 01/03/~ 0 0
## 4 18603 San_Marino SMR Europe 02/03/~ 7 1
## 5 18602 San_Marino SMR Europe 04/03/~ 2 0
## 6 18601 San_Marino SMR Europe 05/03/~ 5 0
## 7 18600 San_Marino SMR Europe 06/03/~ 7 0
## 8 18599 San_Marino SMR Europe 07/03/~ 1 0
## 9 18598 San_Marino SMR Europe 08/03/~ 3 0
## 10 18597 San_Marino SMR Europe 09/03/~ 11 0
## # ... with 98 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
# Checking some other distribution of cases in Europe (neighbouring countries of Andorra and San_marino)
# Spain
countrycode <- "ESP"
case_ESP<- casesByCountry( ,countrycode)
case_ESP1 <- as.data.frame(case_ESP[1])
case_ESP## [[1]]
## # A tibble: 135 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 20202 Spain ESP Europe 01/02/~ 1 0
## 2 20201 Spain ESP Europe 02/02/~ 0 0
## 3 20200 Spain ESP Europe 03/02/~ 0 0
## 4 20199 Spain ESP Europe 04/02/~ 0 0
## 5 20198 Spain ESP Europe 05/02/~ 0 0
## 6 20197 Spain ESP Europe 06/02/~ 0 0
## 7 20196 Spain ESP Europe 07/02/~ 0 0
## 8 20195 Spain ESP Europe 08/02/~ 0 0
## 9 20194 Spain ESP Europe 09/02/~ 0 0
## 10 20193 Spain ESP Europe 10/02/~ 1 0
## # ... with 125 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
# France
countrycode <- "FRA"
case_FRA<- casesByCountry( ,countrycode)
case_FRA## [[1]]
## # A tibble: 143 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 7915 France FRA Europe 25/01/~ 3 0
## 2 7914 France FRA Europe 26/01/~ 0 0
## 3 7913 France FRA Europe 27/01/~ 0 0
## 4 7912 France FRA Europe 28/01/~ 0 0
## 5 7911 France FRA Europe 29/01/~ 1 0
## 6 7910 France FRA Europe 30/01/~ 1 0
## 7 7909 France FRA Europe 31/01/~ 1 0
## 8 7908 France FRA Europe 01/02/~ 0 0
## 9 7907 France FRA Europe 02/02/~ 0 0
## 10 7906 France FRA Europe 03/02/~ 0 0
## # ... with 133 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
# Italy
countrycode <- "ITA"
case_ITA<- casesByCountry( ,countrycode)
case_ITA## [[1]]
## # A tibble: 137 x 9
## ID countriesandter~ countryterritor~ continentexp daterep cases deaths
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 11337 Italy ITA Europe 31/01/~ 3 0
## 2 11336 Italy ITA Europe 01/02/~ 0 0
## 3 11335 Italy ITA Europe 02/02/~ 0 0
## 4 11334 Italy ITA Europe 03/02/~ 0 0
## 5 11333 Italy ITA Europe 04/02/~ 0 0
## 6 11332 Italy ITA Europe 05/02/~ 0 0
## 7 11331 Italy ITA Europe 06/02/~ 0 0
## 8 11330 Italy ITA Europe 07/02/~ 0 0
## 9 11329 Italy ITA Europe 08/02/~ 0 0
## 10 11328 Italy ITA Europe 09/02/~ 0 0
## # ... with 127 more rows, and 2 more variables: popdata2018 <dbl>,
## # date_formatted <chr>
##
## [[2]]
Looking at the case to date graphs of Andorra and San Marino, Andorra has a early increasing and later depleting graph, but shows an abrupt rise in early June with highest number of cases of 79 in a single day, with population of 70000. San Marino as a pretty unusual rise and fall in its cases and does not show any particular pattern/trend of rise or fall in number of cases, but somewhat decreases in the month of June. It has a maximum 36 cases in day in mid February and some similar 34 cases in March, with a population of 33785. The neighboring countries to these two territories may have an impact on its number of cases. Factors like tourism, business with neighboring countries and any other reasons may affect each other. We take into consideration the country of Monaco, which is neighboring country of these two territories and shows similar pattern in cases. Monaco has early rise in cases in February and the two territories show similar case pattern. But the case count in Monaco is very low which can lead to minimal impact but other countries may have an impact on these two territories. We have also considered other neighboring countries of Andorra and San Marino like Spain, Italy and France with 45 to 65 million population, which also have shown similar patterns with initial increase and then reduction in number of cases with their peak in month of March. These countries and factors may have a huge impact on Andorra and San Marino cases.
# Checking the patterns of Europe
data_covid <- data_covid %>% mutate(date_formatted =format(as.POSIXct(data_covid$daterep,format='%d/%m/%Y'),format='%m/%d/%Y')) %>% arrange(date_formatted)
a<-data_covid %>% filter(continentexp == "Europe")
ggplot(data = a, mapping = aes(x =date_formatted, y = cases, fill = countryterritorycode )) + geom_bar(stat = "identity") +theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = "Distribution of cases in Europe" ,caption = "Data source: European Centre for Disease Prevention and Control (ECDC)") + theme(axis.text=element_text(size=12), axis.title=element_text(size=14,face="bold")) + theme(axis.text.x = element_text(angle = 90, size = 5, color = "blue"))
We also checked the trends and patterns of cases to date in the Europe Continent, which also show similar rise in the Europe continent, categorized by countries. It clearly indicated an upward trend in the cases of COVID 19 starting March and then decreasing after reaching its peak in the month on May.